I’m an aspiring Data Scientist with strong foundations in Python, SQL, Excel, and Power BI. I’ve worked on projects involving data cleaning, EDA, feature understanding, and building insight-driven reports/dashboards. I’m…
I’m an aspiring Data Scientist with strong foundations in Python, SQL, Excel, and Power BI. I’ve worked on projects involving data cleaning, EDA, feature understanding, and building insight-driven reports/dashboards. I’m comfortable writing SQL for analysis (joins, CTEs, aggregations) and using Python libraries for analysis and visualization.
I’m looking for an intern/entry-level role where I can learn fast, ship real work, and grow into end-to-end analytics/ML.
Credit Card Fraud Detection (Imbalanced Classification | AUPRC)Built a machine learning pipeline to detect fraudulent credit card transactions from highly imbalanced data.Performed data cleaning, exploratory analysis, and feature scaling/processing where required.Trained and compared multiple models (baseline + advanced) and tuned hyperparameters to improve fraud recall while controlling false alarms.Evaluated using AUPRC (Area Under Precision–Recall Curve) instead of accuracy due to class imbalance; also tracked precision, recall, F1, and confusion matrix.Selected the best model based on AUPRC and created a final evaluation report highlighting trade-offs and business impact (catch more fraud vs fewer false positives).
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